Our method for solving the 3D image super-resolution problem relies on deep neural networks. The universal approximation theorem [1]}, [2]} states that neural networks with at least one hidden layer are universal function approximators. Hence, the hypothesis class represented by neural networks is large enough to accommodate any hypothesis explaining the data. This high model capacity seems to help deep neural networks in attaining state-of-the-art results in many domains [3]}, including image super-resolution [4]}. This is the main reason behind our decision to use neural networks. Interestingly, Dong et al. [4]} show that deep convolutional neural networks for super-resolution are equivalent to previously-used sparse-coding methods [6]}. However, the recent literature indicates that deep neural networks attain better results than handcrafted methods in practice [4]}, [8]}, mainly because the parameters (weights) are learned from data in an end-to-end fashion. Further specific decisions, such as the neural architecture or the loss function, are taken based on empirical observations.
